Sign up to receive free email alerts when patent applications with chosen keywords are publishedSIGN UP

Abstract:

A method for analyzing an electrical power system using fuzzy logic
includes: (a) acquiring data representing a signal of interest of the
power system; (b) analyzing the signal using at least one fuzzy logic
rule; and (c) based on the analysis, detecting and classifying at least
one power system event within the power system.

Claims:

1. A method for analyzing an electrical power system using fuzzy logic,
comprising: (a) acquiring data representing a signal of interest of the
power system; (b) analyzing the data using at least one fuzzy logic rule;
and (c) based on the analysis, detecting and classifying at least one
power system event within the power system.

2. The method of claim 1 further comprising, prior to step (b),
segmenting the signal into a plurality of segments.

3. The method of claim 2 further comprising extracting selected features
from the signal, wherein the analysis is carried out on the extracted
features.

4. The method of claim 1 wherein the fuzzy logic rules are arranged in a
hierarchy of at least two levels.

5. The method of claim 4 wherein the fuzzy logic rules include: (a) phase
level rules which are based on features extracted from a particular phase
on the electric power system; (b) segment level rules which are evaluated
using information from different phases; and (c) capture level rules
which combine truth values obtained from one or more segments.

6. The method of claim 5 wherein the segment level rules are evaluated
using features that are not associated with any single phase on the
electric power system.

7. The method of claim 5 wherein the capture level rules are based on
features that are common to all segments.

8. The method of claim 1 wherein the signal of interest is a derived
parameter.

9. The method of claim 1 wherein the fuzzy logic rules comprise a
plurality of selected rules, wherein the selected rules are collectively
determinative of a particular type of event.

10. The method of claim 1 wherein step (b) includes computing a
confidence value which associates the signal with a category of power
system event.

11. A computer program product comprising one or more computer readable
media having stored thereon a plurality of instructions that, when
executed by one or more processors of a system, causes the one or more
processors to carry out a method comprising: (a) analyzing data
representing a signal of interest in an electrical power system, using at
least one fuzzy logic rule; and (b) based on the analysis, detecting and
classifying at least one power system event within the data.

12. The computer program product of claim 11 wherein the instructions
further cause the one or more processors to, prior to step (a), segment
the signal into a plurality of segments.

13. The computer program product of claim 12 wherein the instructions
further cause the one or more processors to extract selected features
from the signal, wherein the analysis is carried out on the extracted
features.

14. The computer program product of claim 11 wherein the fuzzy logic
rules are arranged in a hierarchy of at least two levels.

15. The computer program product of claim 14 wherein the fuzzy logic
rules include: (a) phase level rules which are based on features
extracted from a particular phase on the electric power system; (b)
segment level rules which are evaluated using information from different
phases; and (c) capture level rules which combine truth values obtained
from one or more segments.

16. The computer program product of claim 5 wherein the segment level
rules are evaluated using features that are not associated with any
single phase on the electric power system.

17. The computer program product of claim 5 wherein the capture level
rules are based on features that are common to all segments.

18. The computer program product of claim 1 wherein the signal of
interest is a derived parameter.

19. The computer program product of claim 1 wherein the fuzzy logic rules
comprise a plurality of selected rules, wherein the selected rules are
collectively determinative of a particular type of event.

20. The computer program product of claim 1 wherein step (b) includes
computing a confidence value which associates the signal with a category
of power system event.

Description:

BACKGROUND OF THE INVENTION

[0001] The present invention relates generally to a method for analyzing
an electrical utility power system, and more particularly to a method for
using fuzzy logic to identify and classify power system events.

[0002] Generally, electrical power originates at a generation station and
is transmitted to a load by a system of conductors and other equipment
that make up an electrical power system. The equipment that makes up an
electrical power system can include switches, reclosers, insulators,
capacitors, transformers, and the like. Over time, or as the result of
some particular cause such as contact of a conductor by vegetation, the
conductors and equipment can cease to operate normally or fail. Sometimes
a failure of conductors or equipment results in an abnormally high
current that can further damage the distribution system or injure end
users or damage end users' devices and equipment.

[0003] In an effort to identify failed devices quickly, utilities may
monitor the operation of an electrical power system by monitoring a
signal indicative of a property such as current. However, conventional
methods of monitoring properties of an electrical power system generally
require human experts to analyze data obtained from the power system. In
this regard, experts may not be available for analysis when needed and
can be very expensive. Furthermore, automated analytical systems using
only classical (crisp) logic may not work well when presented with data
containing uncertainties introduced by missing data and inaccurate
measurements or may not sufficiently encode the human knowledge needed to
analyze data. Thus, this invention provides a method for automatically
identifying and characterizing power system events in a reliable and
efficient manner without overwhelming the user with data. This is
important for safe, reliable, and economical operation of the electric
power system. It allows efficient determination of what caused the events
observed on a power system. This invention allows system operators or
others to better assess the health of the system, take corrective
actions, and restore service to customers, while minimizing adverse
effects to the system itself or to personnel.

BRIEF SUMMARY OF THE INVENTION

[0004] These and other shortcomings of the prior art are addressed by the
present invention, which according to one aspect provides a method for
analyzing an electrical power system using fuzzy logic. The method
includes: (a) monitoring data representing a signal of interest of the
power system; (b) analyzing the signal using at least one fuzzy logic
rule; and (c) based on the analysis, detecting and classifying at least
one power system event within the power system.

[0005] According to another aspect of the invention, a computer program
product includes one or more computer readable media having stored
thereon a plurality of instructions that, when executed by one or more
processors of a system, causes the one or more processors to carry out a
method including: (a) analyzing data representing a signal of interest in
an electrical power system, using at least one fuzzy logic rule; and (b)
based on the analysis, detecting and classifying at least one power
system event within the data.

BRIEF DESCRIPTION OF THE DRAWINGS

[0006] The subject matter that is regarded as the invention may be best
understood by reference to the following description taken in conjunction
with the accompanying Figures in which:

[0007] FIG. 1 is a schematic block diagram of a monitoring system for an
electrical power system according to one aspect of the present invention;

[0008] FIG. 2 is a schematic diagram showing a process for analyzing a
power system using fuzzy logic in accordance with the present invention;

[0009] FIG. 3 is a diagram used to evaluate a fuzzy logic membership
function named "big change in reactive power";

[0010] FIG. 4 is a diagram used to evaluate a fuzzy logic membership
function named "small change in reactive power";

[0011] FIG. 5 is a diagram used to evaluate a fuzzy logic membership
function named "small change in real power";

[0012] FIG. 6 is a diagram used to evaluate a fuzzy logic membership
function named "medium percent change in voltage";

[0013] FIG. 7 is a diagram used to evaluate a fuzzy logic membership
function named "small relative change in reactive power";

[0014] FIG. 8 is a diagram used to evaluate a fuzzy logic membership
function named "degree of confidence";

[0015] FIG. 9 is a diagram used to evaluate a fuzzy logic membership
function named "clipped degree of confidence"; and

[0016] FIG. 10 is a diagram used to evaluate a fuzzy logic membership
function named "combined degree of confidence".

DETAILED DESCRIPTION OF THE INVENTION

[0017] Referring to the drawings wherein identical reference numerals
denote the same elements throughout the various views, an exemplary power
system incorporating a fuzzy logic analysis system constructed according
to an aspect of the present invention is illustrated in FIG. 1, coupled
to a feeder line 12 of an electrical power system. The feeder line 12
receives power from an AC power source, such as a generating station 14,
through a substation 16. Other feeder lines (not shown) may also receive
power from the generating station 14 and exit the substation 16. The
feeder line 12 delivers power from the substation 16 to a variety of
utility customers, such as customer 18.

[0018] Altogether, the generating station 14, the substation 16, and
feeder line 12 illustrate a portion of an electrical utility's power
system. As used herein, the term "line" refers to one or more conductors
grouped together for conducting electrical power from a first point to a
second point. As used herein, the term "conductor" refers to a material
that provides a path for electricity and includes a wire, a group of
wires, or other conductive material.

[0019] Although the invention is described as implemented in an electrical
power distribution system, it will be understood that it may be
implemented in any portion of an electric power system, including but not
limited to generating stations, substations, transmission lines, primary
and secondary distribution lines, and customer facilities.

[0020] Most typical power systems generate and distribute power using a
three-phase system. Thus, the feeder line 12 may deliver power over three
conductors that each conducts a phase A, B, or C. The feeder line 12 may
also have a fourth conductor which is referred to as the neutral. For
convenience, power system 20 illustrated herein is such a three-phase
system that includes a neutral conductor.

[0021] In the illustrated example, the fuzzy logic analysis system
includes a data acquisition unit 10, which is shown at a substation 16 in
the illustrated embodiment. It is noted that the arcing-event-detection
system and method of the present invention need not include the data
acquisition unit 10, but may instead be implemented as software and/or
hardware which analyzes data provided from an outside source, such as
existing measurement equipment. The present invention may be used at any
location within a system of power lines, i.e. generating stations,
substations, transmission lines, primary and secondary distribution
lines, and customer facilities. Furthermore, multiple data acquisition
units 10 can be placed at selected intervals in one or more locations of
interest in a power system. For example, data acquisition units 10 could
be placed at a substation as well as spread along a line at various
distances from a substation such as at 2, 4, 6, and 8 miles from the
substation. This "sectionalization" may be useful in determining the
specific location of a fault. In this regard, if a fault occurs between
miles 4 and 6 from a substation, differences in the signals generated by
the data acquisition units 10 positioned at miles 4 and 6 may be useful
for determining where the fault occurred relative to miles 4 and 6.

[0022] Between the substation 16 and the customer 18, the feeder line 12
may be subjected to a variety of different types of events, conditions,
activities, and faults. Some typical events, conditions, activities, and
faults are illustrated in FIG. 1, specifically, a downed conductor 22, a
dangling conductor 24, contact of vegetation such as a tree 25 or other
object with the feeder line 12, and a broken insulator 27. The system may
also be subject to other disrupting events, such as an overcurrent event
26 or a switching event performed by a conventional recloser 28 or the
like. In addition to conventional faults, the electrical power system is
also subject to mis-operation or partial failure of components. For
example, devices such as a switching controller for a capacitor bank or a
tap changer for a transformer can enter a failure mode in which switching
occurs too often. This can cause unacceptable power quality for the
customer 18 and wear out the switching equipment, which eventually
damages the switching equipment and/or related equipment.

[0023] The data acquisition unit 10 includes a monitoring device, such as
a sensor or transducer 30, coupled to feeder line 12 as indicated
schematically by line 32. The term "monitoring device" is broadly defined
herein to include sensing devices, detecting devices, and any other
structurally equivalent device or system understood to be interchangeable
therewith by those skilled in the art. The illustrated transducer 30
senses or monitors several line parameters, such as line voltages for
each phase (line-to-line VLL or line-to-neutral VLN), or load
current IL flowing through line 12 for each phase conductor or
neutral conductor. Any subset of the 6 voltages or 4 currents measurable
in a three-phase system may be monitored. The present invention may also
be used with single-phase systems. For instance, in response to
monitoring a load current IL and a line-to-neutral (phase) voltage,
transducer 30 produces a parameter signal, here, a signal 34 that is
indicative of dual load current and phase voltage. The transducer 30 may
be a conventional transducer or an equivalent device, such as a multiple
phase current measuring device typically having one current transformer
per phase, plus one on the neutral conductor, of the feeder line 12, and
a multiple phase voltage measuring device, measuring the line-to-neutral
voltages for each phase of line 12. Moreover, the data acquisition unit
10 may receive transducer signals from already existing current and
voltage sensors. For example, if only a single phase of the voltage is
measured by transducer 30 or another transducer (not shown), the data
acquisition unit 10 may be equipped with conventional hardware or
software of a known type to derive the other two phases. That is, knowing
one phase voltage on a three-phase system, the other two phases may be
obtained by applying the appropriate plus/minus appropriate (e.g.,
120°) phase shift to the monitored phase voltage. It is also
conceivable that other parameters, e.g. power factor, of the power
flowing through line 12 may be measured with suitable transducers.

[0024] The data acquisition unit 10 may also include surge protection, for
example, a surge suppressor or protector 36. The surge protector 36 may
be supplied either with the transducer 30, as illustrated, or as a
separate component. The surge protector 36 protects the data acquisition
unit 10 from power surges on the feeder line 12, such as those caused by
lightning strikes or the like.

[0025] The data acquisition unit 10 may include a signal conditioner 38
for filtering and amplifying the signal 34 to provide a clean,
conditioned signal 40. Preferably, the signal conditioner 38 includes one
or more filters (e.g. low-pass, band-pass, high-pass, notch) for removing
frequency components not of interest for the analysis such as signal
noise. The data acquisition unit 10 may be used with a single frequency
in the spectrum, or a combination of frequencies.

[0026] The signal conditioner 38 may also amplify the parameter signals 34
for the appropriate range required by an analog-to-digital (A/D)
converter 42. For example, the current flowing on the power system 20 may
have a dynamic range of 10 to 10,000 Amps, which transducer 30 may
convert into a time-varying voltage signal of, for example, +/-25 volts,
whereas the A/D converter 42 may accept voltages of +/-10 volts. In this
case the signal conditioner 38 appropriately converts and scales these
signals for conversion by the A/D converter 42 from an analog signal 40
into a digital parameter signal 44.

[0027] When the transducer 30 is an analog device, the data acquisition
unit 10 includes the illustrated discrete A/D converter 42. The
transducer 30 may also be implemented as a digital device which
incorporates the signal conditioning function of conditioner 38 and the
analog-to-digital conversion function of the A/D converter 42.

[0028] The digital parameter signal 44 is supplied to a computing device
for analysis. An example of a suitable computing device includes a
conventional microcomputer (sometimes referred to as a personal computer
or "PC"). However, any device capable of executing a program instruction
set to analyze the digital parameter signal may be used. As shown in FIG.
1, a computing device 48 such as a "single board computer" is directly
connected to the data acquisition unit 10 and may be placed inside a
common housing or container with the data acquisition unit 10, or
otherwise integrated with the data acquisition unit 10, to form a
self-contained detection and analysis unit 50. Alternatively or in
addition to the computing unit 48, an external computing unit 48' may be
connected to the data acquisition unit 10 using a direct connection such
as a serial or parallel cable, wireless link, or the like. Furthermore,
the data acquisition unit 10 may be connected to a remote computing unit
48'' through a network 52 e.g., a local area network (LAN), a wide area
network (WAN), or the Internet. Also, it is noted that the fuzzy logic
analysis method described herein may be integrated into existing systems
which already include data collection and/or processing capability. For
example, known types of relays, power quality meters, and other equipment
used in power transmission or distribution often contain
microprocessor-based electronics suitable for performing the analysis.

[0029] The fuzzy logic analysis method shall now be described further.
FIG. 2 illustrates an algorithm for fuzzy logic analysis. First, a signal
representative of one or more aspects of a power system (for example the
digital parameter signal 44 described above) is segmented (block 100).
This may be done, for example, using Kalman filtering. Next, features of
interest in the data structure are extracted (block 102) for further
analysis. Finally, the extracted features are analyzed using fuzzy logic
to identify and classify power system events (block 104). The method of
the present invention may also be used to classify features that have
already been extracted from existing data.

[0030] In one example, segmentation was done by detecting edges in
differenced RMS voltages, differenced RMS currents, real power and
reactive power signals. Using these edges, the measured signals were
broken into segments.

[0031] In one example, two kinds of features were extracted: generic
features and event specific features.

[0032] Generic features are features extracted from the signals in a
segment irrespective of the type of signal. These features may provide
some statistical information about the signal such as maximum and minimum
value, mean value etc. over the duration of the event or they may provide
information defining the shapes observed in the signals.

[0033] Event specific features are specific characteristics required to
ascertain if a segment within a capture was caused by a specific power
system activity like arcing or capacitor switching. Simple shape analysis
will not always be sufficient to classify these captures. An expert
sometimes needs to do a detailed analysis of the waveforms before
classifying a capture. Based on this reasoning, specialized algorithms
for obtaining features characteristic of certain event types may be
employed.

[0034] An example will now be discussed of the fuzzy logic classification
process. The following example shows how fuzzy logic rules may be applied
to detect a three phase capacitor switching on event. The rules and
membership functions provided here are only for illustrative purposes and
do not represent the complete set of rules and used by the invention.

[0035] The inputs (see table below) are assumed to have already been
extracted from the data for each phase. These are called features. The
features are the "evidence" or inputs based on which the rules will be
evaluated. From here on, the word "rules" is used to represent both fuzzy
rules and crisp (classical) rules unless otherwise indicated.

TABLE-US-00001
TABLE 1
Features
Feature Type Description
1 Reactive power step Boolean True if a step down in reactive
down power was seen
2 change in reactive Real change of amplitude in reactive
power power
3 change in real power Real change of amplitude in real power
4 Voltage transient Boolean True if a voltage transient was
observed observed
5 Voltage step up Boolean True if a step up in the voltage was
seen
6 change in voltage Real Size of change in voltage
7 Relative change in Real Percentage balance in the reactive
reactive power power across three phases.

[0036] Due to the complex nature of the analysis, the fuzzy expert is
hierarchical in nature and rules are evaluated at different levels in the
hierarchy. Rules at the bottom most level in the hierarchy use features
that are extracted directly from the data as inputs. Rules at higher
levels in the hierarchy use truth values that were computed earlier and
also features that were obtained from the data.

[0037] The example will describe evaluation of rules in three stages: (1)
phase level rules; (2) segment level rules/multi-phase rules and (3)
capture level rules. Though the example shows the rules to be organized
in three levels, it can be easily modified to use fewer or more levels.

[0038] Phase Level Rules

[0039] Phase level rules use as inputs features extracted from a
particular phase on the electric power system 20 (i.e. phase A, phase B,
phase C). Then the corresponding truth values are calculated. The table
below shows example phase level rules.

TABLE-US-00002
TABLE 2
Phase level rules
Rule
Truth value holder type Rule
1 VARS_Step_Down Crisp There was a Reactive power step down on
this phase
2 BIG_VAR_change Fuzzy There was a big change in reactive power
on this phase
3 Capacitor_On_VAR_Behaviour Fuzzy There was BIG_VAR_change and
VARS_Step_Down
4 No_Real_Power_change Fuzzy There was small change in real power on
this phase
5 No_Reactive_Power_change Fuzzy There was small change in reactive power
on this phase
6 No_Real_Reactive_Power_change Fuzzy No_Real_Power_change and
No_Reactive_Power_change on this phase
7 V_Transient_Present Crisp There was Voltage transient observed on
this phase
8 V_Step_Up Crisp There was Voltage step up on this phase
9 MEDIUM_V_change Fuzzy There was a medium percentage change in
voltage on this phase

[0040] The above table should be read as follows:

[0041] Truth value holder=TRUTH (Rule). Rule 2 in the above table must be
read as BIG_VAR_change=TRUTH ("There was a big change in reactive power
on this phase"). The rules in Table 2 summarize some typical observations
that need to be made on every phase to identify a capacitor switching on
event. Different observations may be used to identify different types of
power system events. The observations would be based on human expert
knowledge of what observations are relevant to specific events.

[0042] A capacitor switching on event on the monitored feeder is typically
recognized by VARS (reactive power) stepping down, a big change in VARS,
and small or no change in real power. The capacitor switching on event on
an adjacent feeder can be recognized by observing if a voltage transient
was observed and there was a medium percentage change in voltage and the
voltage stepped up and there was not much change in VARS or real power.

[0043] Rules 2, 4, 5 and 9 in Table 2 use linguistic descriptors like
"big", "small" and "medium". The following membership functions (Table 3,
FIGS. 3-6) are required to evaluate the truth of these rules.

[0045] Using feature values in Table 4 and the membership functions in
Table 3, the membership values can be computed. The calculated membership
values are shown in Table 5.

[0046] For example consider the membership function "medium percentage
change in voltage". The membership value is needed for evaluation of rule
9 in Table 2. The corresponding input feature is "percentage change in
voltage". The values for this feature for the three phases were 0.35%,
0.40%, and 0.15% respectively. To determine the membership degree, they
have to be calculated using the relationship defined in FIG. 6. Since the
percentage change values for phase A and B lie in the `flat` region of
the membership function (between 0.1% and 1%), their membership degree
are 1.0. However, the percentage change in voltage for phase C is 0.15%,
this lies in the `transition` region of the membership function (between
0.1% and 0.3%). Since the membership degree changes linearly in this
region, the membership degree (see example point "X" in FIG. 6) can
easily be calculated as (0.15-0.1)/(0.3-0.1)=0.25. The calculated values
can be seen in row 4 of the table below.

[0047] The calculated truth values for the nine rules in Table 2 are shown
in Table 6. It can be seen that the truth values are calculated for each
phase. Rules 1, 7 and 8 are crisp rules and the resulting truth values
are either 0.0 or 1.0. The evaluation of these rules are straight forward
as the rules check if there was a step down in VARS, if there was a
voltage transient and if there was a step up in voltage for the
corresponding phases. These truth values can be obtained directly from
the features 1, 4 and 5 in Table 4.

[0048] Rules 2, 4, 5 and 9 use the results of fuzzy membership values
calculated in Table 5. These results are directly entered into the rows
2, 4, 5 and 9 of Table 6 below. Take for example rule 2, which can be
represented as: BIG_VAR_change=TRUTH ("There was a big change in reactive
power on this phase"). As explained in the previous section this is same
as BIG_VAR_change=μ.sub.BigVARS (change in reactive power). Therefore:

[0049] Rules 3 and 6 in Table 2 are complex rules that make use of truth
values that have already been calculated. Take for example rule 3:
Capacitor_On_VAR_Behaviour=TRUTH ("There was BIG_VAR_change AND
VARS_Step_Down").

[0050] Here the AND conjunction operator is used to combine two conditions
"BIG_VAR_change" and "VARS_Step_Down". Unlike in Boolean logic, the AND
operator is a fuzzy conjunction operator and the result of the AND
operation can be a value between 0.0 and 1.0. The function "min"
(minimum) was chosen as the fuzzy conjunction operator. Even though the
"min" function was chosen, a person skilled in the art may easily
substitute it with other equivalent functions. Now
Capacitor_On_VAR_Behaviour can be computed as follows:

[0051] Capacitor_On_VAR_Behaviour=min (BIG_VAR_change, VARS_Step_Down).
BIG_VAR_change and VARS_Step_Down values have already been calculated and
can be obtained from rows 1 and 2 of Table 6:

For phase A, Capacitor_On_VAR_Behaviour=min(1.0, 1.0)=1.0

For phase B, Capacitor_On_VAR_Behaviour=min(1.0, 1.0)=1.0

For phase C, Capacitor_On_VAR_Behaviour=min(1.0, 1.0)=1.0

[0052] Similarly, the truth values for rule 6 can also be computed. Table
6 shows the final truth values after all the phase level rules have been
evaluated.

[0053] Segment Level Rules/Multi-Phase Rules

[0054] The data being analyzed may be broken one or more segments. Each of
these segments may contain related or non related power system events.
Each power system event may involve one or more phases and the neutral.
It is important to analyze the relative behavior of different phases in a
segment to associate the data being processed with a particular power
system event type. For the sake of simplicity, this example assumes that
the data contains only a single segment.

[0055] In the previous step, truth values were calculated for each phase,
independent of one another. The segment level rules combine truth values
that were calculated from different phases. They may also use as inputs,
features that are not associated with any single phase (phase-independent
features). An example for evaluating segment level rules for the
capacitor switching on detection is discussed in the following
paragraphs.

[0056] Example of segment level rules that may be used for the detection
of three phase capacitor switching on event are listed in Table 7.

TABLE-US-00007
TABLE 7
Segment level rules/multi-phase rules
Truth Value Holder Rule
1 Balanced_VAR_change The Relative change in reactive power
was small
2 3Phase_Capacitor_On_VAR_Behaviour There was
Capacitor_On_VAR_Behaviour on all
phases
3 No_3Phase_Real_Power_change There was No_Real_Power_change on all
of the phases
4 No_3Phase_Real_Reactive_Power_change There was
No_Real_Reactive_Power_change on all
of the phases
5 3Phase_Capacitor_On_V_Behaviour There was V_Transient_Present in at
least
one phase AND V_Step_Up on all phases
AND MEDIUM_V_change on all phases

[0057] Rule 1 in Table 7 requires looking up the membership degree of the
feature "Relative change in reactive power" in the membership function
"small relative change in reactive power". The membership function is
shown in FIG. 7. The value for the phase independent feature "Relative
change in reactive power" is 0.05 for this example (Table 4). Now,
Balanced_VAR_change can be calculated as
Balanced_VAR_change=μ.sub.SmallRel(5%)=1.0.

[0058] Rules 2, 3 and 4 in Table 7 use the truth values
Capacitor_On_VAR_Behaviour, No_Real_Power_change, and
No_Real_Reactive_Power_change that have already been calculated for each
phase in the previous step (Table 6). However they also involve a
condition "on all phases". This condition can be met by using the AND
conjunction operator combining the truth values for the three phases. For
example rule 2 in Table 7 should be expanded as:

[0059] 3Phase_Capacitor_On_VAR_Behaviour=TRUTH ("There was
Capacitor_On_VAR_Behaviour on phase A AND There was
Capacitor_On_VAR_Behaviour on phase B AND There was
Capacitor_On_VAR_Behaviour on phase C"). As explained in the previous
section, the AND condition may be replaced by the fuzzy conjunction
operator "min". The truth value 3Phase_Capacitor_On_VAR_Behaviour may now
be calculated as:

[0061] In a similar fashion, No_Real_Power_change and
No_Real_Reactive_Power_change may also be calculated. These values are
shown in Table 8 below. Segment level rule 5 in Table 7 is a more complex
rule that combines three conditions using the conjunction operator "AND".
It also involves truth values from different phases for each condition
that is combined using the AND operator. The first condition "there was
V_Transient_Present in at least one phase", uses the truth value
V_Transient_Present that has already been computed for each phase in the
previous step (Table 6). However, the condition "in at least one phase"
should be interpreted as follows:

[0062] "There was V_Transient_Present on phase A, OR There was
V_Transient_Present on phase B, OR There was V_Transient_Present on phase
C"

[0063] The three truth values are combined using the "OR" condition. Since
these are truth values, it is not possible to evaluate the statement
using the "OR" operator of Boolean. Instead, the fuzzy disjunction
operator max (maximum) is used. The reason for choosing this operator is
understood by those skilled in the art. A person skilled in the art may
substitute the max operator with an equivalent fuzzy disjunction
operator. Now rule 5 can be expanded as follows:

[0065] The above example of rule evaluation demonstrates the complex
nature of the rules, which are handled by a parser and inference engine.
It is noted that rule grammar shown in the examples here are simplified
for easy understanding.

[0066] Capture Level Rules

[0067] Capture level rules look at the data as a whole and combine the
truth values obtained from one or more segments. They use as inputs the
truth values that were obtained from the segment level rules or they may
also use features that are independent of the segment, i.e. common to all
segments. The three phase capacitor switching on detection example will
be continued and the evaluation of the relevant capture level rules will
be shown. Table 9 shows example rules that may be used at the capture
level for three phase capacitor switching detection. The last column in
the table under the heading "Degree" is the degree to which the fuzzy
expert system believes that data belongs to the corresponding category.
In other words it is the truth in the statements "Data caused by category
Monitored Feeder 3 Phase Capacitor On" and "Data caused by category
Adjacent Feeder 3 Phase Capacitor On".

[0071] It can be seen that based on the rules and features in this
example, the fuzzy expert system would believe to a high degree that the
data was caused by a monitored feeder three phase capacitor switching on.
It also demonstrates how the expert's knowledge can be captured by fuzzy
rules and be used to classify power system data.

[0072] It should be noted that there can be more than one rule that points
to the same category and there may be confidence degree associated with
each of these rules. The result of evaluating each such rule is not a
single truth value but a membership function. They may be combined using
superimposition by applying the "max" disjunction operator. A more
sophisticated method called defuzzification is required to compute the
final truth values assigned to each category. The current embodiment
implements one such defuzzification method. Consider for example rules 1,
2 in Table 10 below. These are variations of rule 1 in Table 9. They have
a confidence degree associated with them. The rule 1 in Table 10 may be
read as:

[0073] "IF Balanced_VAR_change AND 3Phase_Capacitor_On_VAR_Behaviour AND
3Phase_Capacitor_On_V_Behaviour THEN the data was caused by Monitored
Feeder 3 Phase Capacitor On with a Medium degree of confidence."

TABLE-US-00010
TABLE 10
Segment level rules/multi-phase rules with confidence degrees
Rule Category Truth in Evidence
1 IF Balanced_VAR_change AND THEN Monitored Feeder 3 0.25
3Phase_Capacitor_On_VAR_Behaviour Phase Capacitor On with
AND Medium degree of confidence
3Phase_Capacitor_On_V_Behaviour
2 IF Balanced_VAR_change AND THEN Monitored Feeder 3 1.0
3Phase_Capacitor_On_VAR_Behaviour Phase Capacitor On with High
AND degree of confidence
No_3Phase_Real_Power_change

[0074] In Table 10, the "IF" part of the two rules differ; rule 1 focuses
on "3Phase_Capacitor_On_V_Behaviour" while rule 2 focuses on
"No--3Phase_Real_Power_change". Experts believe that the evidences
in rule 2 (not to be confused with the truth in the evidence) are
stronger than evidences in rule 1. Consequently, rule 2 was assigned
higher confidence degree than rule 1. The confidence degrees are static
and are part of the rule. They are assigned to the rule when the rules
were created; they are not computed on the fly. These confidence degrees
reflect the degree of confidence the expert has on the ability of a
particular rule in identifying a particular category of event. FIG. 8
shows Medium and High fuzzy membership functions for degree of
confidence.

[0075] Since the THEN part of rules 1 and 2 in Table 10 contains a fuzzy
membership function, unlike the rules in Table 9, the truth values
calculated from the IF part of the rules cannot be used directly. First
the results of rule 1 and 2 in Table 10 need to be combined to generate
degrees of confidence. Then defuzzification has to be performed on the
degree of confidence to compute the final confidence value.

[0076] A method called "clipping" will be shown in this example for
combining the results of the two rules. It should be noted that this
method can easily be substituted by other methods such as scaling by a
person familiar with the art. Clipping involves truncating or clipping
the height of the membership function in the THEN part of the rule at a
value equal to the truth value that was calculated for the IF part of the
rule (i.e. based on the truth in the evidence). In this case, the Medium
and High degree of confidence membership functions in the THEN part of
the rules have to be clipped at values 0.25 and 1.0 that were calculated
as the truth in the evidence (truth in IF section). FIG. 9 shows the
clipped membership functions.

[0077] The clipped membership functions are then combined using a maximum
operator on a point by point basis, i.e. the two output clipped
membership functions are superimposed. The theoretical reason behind the
clipping, combination and defuzzification techniques are understood by
those skilled in the art. FIG. 10 shows the combined output degree of
confidence membership function.

[0078] Combing the results of the two rules produces a membership function
but not a single confidence value. A single confidence value is needed
for every category type. In this case a single confidence value needs to
be calculated for "Monitored Feeder 3 Phase Capacitor On" category. A
defuzzification method called Center of Area (COA) is used to obtain the
final confidence value (yfinal) from the confidence membership
function μComb. The following formula may be used:

y final = i μ Comb ( y i ) × y i
i μ Comb ( y i ) ##EQU00001##

[0079] In order to apply the above formula, the membership function needs
to be sampled at discrete points (yi). Then the above formula can be
used. Using the COA defuzzification method, the final confidence value
may be computed to be 0.8. This is the confidence with which the expert
system classifies the example data to belong to the category "Monitored
Feeder 3 Phase Capacitor On" based on the rules. Even though COA method
was shown here for defuzzification, a person skilled in the art may use
other methods such as centroid or Mean of Maximum (MOM) defuzzification.

[0080] Fuzzy rules at the phase, segment and capture level may similarly
be designed to detect other power system events such as but not limited
to, monitored feeder capacitor switching off, monitored feeder
overcurrent event, monitored feeder arcing event, monitored feeder motor
start, adjacent feeder overcurrent etc. The expert system may compute
values to each category using methods described earlier. Take for example
a hypothetical set of such final confidence values that were assigned to
various categories shown in the Table 11 below.

[0081] The fuzzy expert system needs to choose one of the categories as
the category that caused the data being analyzed. In one embodiment of
the invention, the category with the maximum confidence value was chosen
as the final classification result. In this example, category 1,
"Monitored feeder capacitor switching on" would be chosen as the final
classification or category. This is the category to which the expert
system would most confidently associate the data.

[0082] There are some special scenarios to be considered, for example:

[0083] When none of the confidence values is greater than a threshold
value, the expert system will not associate the data with any of the
categories.

[0084] When more than one category is assigned a confidence value greater
than the threshold value, a conflict resolution strategy is used to
select the most appropriate category. The conflict resolution strategy is
based on various conditions such as but not limited to:

[0085] Possibility of producing false alarms: For example if a high
confidence value was obtained for both "monitored feeder capacitor
switching on" and "capacitor switch arcing", preference may be given to
"monitored feeder capacitor switching on" because "capacitor switch
arcing" is an abnormal event and will be considered a false alarm if no
actual arcing occurred. Such preferences may be assigned based on, but
not limited to classification statistics and user defined preferences.

[0086] Perceived importance of a particular category: When multiple
segments are detected in the data, each of these segments may have been
caused by independent power system events. Take for example the expert
system assigned a high confidence value to "monitored feeder capacitor
on" event in one segment and a high confidence value for "monitor feeder
motor start" event on another segment. Now it is difficult to assign a
single category name for the whole data. In such cases, the category that
is perceived as being more important to report will be given preference.
Such preferences may be user defined.

[0087] Missing data information: In some situations when only partial
information is available, there is a potential for conflicting category
assignment. Take for example, the case of missing voltage measurements.
Typically voltage measurements are available for all the three phases,
but in certain locations, the voltage measurements may not be available
in one or more phases either because there was no sensor provided or
because of a faulty sensor/connection. Normally, uniform dips in voltages
on all three phases without corresponding changes in current measurements
are associated with motor starts on an adjacent feeder. When one of the
voltages is missing, it is difficult to determine if the event was caused
by an adjacent feeder motor or by an overcurrent on transmission system.
In such cases it is not uncommon for the algorithm to output high
confidence values for both categories. Here the knowledge of missing
voltage and classification statistics may be used to give preference to
one category over the other. It should be noted that missing data is not
limited to voltage measurements, current or other measurement may also be
missing. When a measurement is missing, all signals derived from these
measurements are also considered missing.

[0088] The foregoing has described a method and system for identification
of events on a power system using fuzzy logic. While specific embodiments
of the present invention have been described, it will be apparent to
those skilled in the art that various modifications thereto can be made
without departing from the spirit and scope of the invention.
Accordingly, the foregoing description of the preferred embodiment of the
invention and the best mode for practicing the invention are provided for
the purpose of illustration only and not for the purpose of limitation.

Patent applications by Billy Don Russell, College Station, TX US

Patent applications by Carl L. Benner, Bryan, TX US

Patent applications by Karthick Muthu-Manivannan, College Station, TX US